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              <h1 class="title is-1 publication-title">
                AgentTuning: Enabling Generalized Agent Abilities For LLMs
              </h1>
              <div class="is-size-5 publication-authors">
                <!-- Paper authors -->
                <span class="author-block">
                  <a href="mailto:zengaohan@gmail.com" target="_blank"
                    >Aohan Zeng</a
                  ><sup>†</sup><sup>§</sup><sup>*</sup>,</span
                >
                <span class="author-block">
                  <a
                    href="mailto:liu-md20@mails.tsinghua.edu.cn"
                    target="_blank"
                    >Mingdao Liu</a
                  ><sup>†</sup><sup>*</sup>,
                  <span class="author-block">
                    <a
                      href="mailto:lu-r21@mails.tsinghua.edu.cn"
                      target="_blank"
                      >Rui Lu</a
                    ><sup>†</sup><sup>*</sup>,
                  </span>
                  <span class="author-block">
                    <a
                      href="mailto:wangbw21@mails.tsinghua.edu.cn"
                      target="_blank"
                      >Bowen Wang</a
                    ><sup>†</sup>,
                  </span>
                  <span class="author-block">
                    <a
                      href="mailto:liuxiao21@mails.tsinghua.edu.cn"
                      target="_blank"
                      >Xiao Liu</a
                    ><sup>†</sup><sup>§</sup>,
                  </span>
                  <span class="author-block">
                    <a
                      href="mailto:yuxiaod@mail.tsinghua.edu.cn"
                      target="_blank"
                      >Yuxiao Dong</a
                    ><sup>†</sup>,
                  </span>
                  <span class="author-block">
                    <a
                      href="mailto:jietang@mail.tsinghua.edu.cn"
                      target="_blank"
                      >Jie Tang</a
                    ><sup>†</sup>
                  </span>
                </span>
              </div>

              <div class="is-size-5 publication-authors">
                <span class="author-block">
                  <sup>†</sup>Tsinghua University, <sup>§</sup>Zhipu.AI
                  <span class="eql-cntrb"
                    ><small><br /><sup>*</sup>Equal contribution</small></span
                  >
                  <span class="eql-cntrb internship"
                    ><small
                      ><br /><sup>1</sup>Work is done during the internship in
                      Zhipu.AI of Mingdao Liu, Rui Lu, Bowen Wang</small
                    ></span
                  >
                  <!--                             <br/><span class="author-block"><a href="mailto:yue.149@osu.edu">zengaohan@gmail.com</a>
                      , <a href="mailto:wenhuchen@uwaterloo.ca">wenhuchen@uwaterloo.ca</a> </span> -->
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                      href="https://huggingface.co/THUDM/agentlm-70b"
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                      <span>🤗 Model (AgentLM)</span>
                    </a>
                  </span>

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                    <a
                      href="https://huggingface.co/datasets/THUDM/AgentInstruct"
                      target="_blank"
                      class="external-link button is-normal is-rounded is-dark"
                    >
                      <span>🤗 Dataset (AgentInstruct)</span>
                    </a>
                  </span>

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                      <span>Code</span>
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                      <span class="icon">
                        <i class="ai ai-arxiv"></i>
                      </span>
                      <span>arXiv</span>
                    </a>
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    </section>

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            <h2 class="title is-3">Abstract</h2>
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              <p>
                Open large language models (LLMs) with great performance in
                various tasks have significantly advanced the development of
                LLMs. However, they are far inferior to commercial models such
                as ChatGPT and GPT-4 when acting as agents to tackle complex
                tasks in the real world. These agent tasks employ LLMs as the
                central controller responsible for planning, memorization, and
                tool utilization, necessitating both fine-grained prompting
                methods and robust LLMs to achieve satisfactory performance.
                Though many prompting methods have been proposed to complete
                particular agent tasks, there is lack of research focusing on
                improving the agent capabilities of LLMs themselves without
                compromising their general abilities. In this work, we present
                <b>AgentTuning</b>, a simple and general method to enhance the
                agent abilities of LLMs while maintaining their general LLM
                capabilities. We construct <b>AgentInstruct</b>, a lightweight
                instruction-tuning dataset containing high-quality interaction
                trajectories. We employ a hybrid instructiontuning strategy by
                combining <b>AgentInstruct</b> with open-source instructions
                from general domains. <b>AgentTuning</b> is used to
                instruction-tune the Llama 2 series, resulting in
                <b>AgentLM</b>. Our evaluations show that
                <b>AgentTuning</b> enables LLMs’ agent capabilities without
                compromising general abilities. The <b>AgentLM-70B</b> is
                comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating
                generalized agent capabilities. We open source the
                <b>AgentInstruct</b> dataset and <b>AgentLM-7B</b>, <b>13B</b>,
                and <b>70B</b> models at
                <a
                  href="https://github.com/THUDM/AgentTuning"
                  style="color: blue"
                  >https://github.com/THUDM/AgentTuning</a
                >, serving open and powerful alternatives to commercial LLMs for
                agent tasks.
              </p>
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              <h2 class="title is-3">Overall Results</h2>
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          <p>
            <b>AgentTuning</b> represents the very first attempt to
            instruction-tune LLMs using interaction trajectories across multiple
            agent tasks. Evaluation results indicate that
            <b>AgentTuning</b> enables the agent capabilities of LLMs with
            robust generalization on unseen agent tasks while remaining good on
            general language abilities. We have open-sourced the
            <b>AgentInstruct</b>
            dataset and <b>AgentLM</b>.
          </p>
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            <h2 class="title is-3">Method</h2>
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          <p>
            An overview of <b>AgentInstruct</b> and <b>AgentTuning</b>. The
            construction of <b>AgentInstruct</b>, consisting of instruction
            generation, trajectory interaction, and trajectory filter.
            <b>AgentLM</b> is finetuned using a mixture of
            <b>AgentInstruct</b> and general-domain instructions.
          </p>
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            <h2 class="title is-3">Our Dataset: AgentInstruct</h2>
          </div>
          <br />
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          <table>
            <caption>
              Overview of our
              <b>AgentInstruct</b>
              dataset which includes 1,866 trajectories from 6 agents tasks.
            </caption>
            <thead>
              <tr>
                <th>Task</th>
                <th>Inst. From</th>
                <th># Inst.</th>
                <th># Filt. Traj.</th>
                <th>Avg # Filt. Traj. Turns</th>
                <th>Ratio</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td>ALFWorld</td>
                <td>Train split</td>
                <td>954</td>
                <td>336</td>
                <td>13.52</td>
                <td>35.2%</td>
              </tr>
              <tr>
                <td>WebShop</td>
                <td>Train split</td>
                <td>1,485</td>
                <td>351</td>
                <td>3.68</td>
                <td>23.6%</td>
              </tr>
              <tr>
                <td>Mind2Web</td>
                <td>Train split</td>
                <td>23,378</td>
                <td>122</td>
                <td>1.00</td>
                <td>0.52%</td>
              </tr>
              <tr>
                <td>Knowledge Graph</td>
                <td>Train split</td>
                <td>2,501</td>
                <td>324</td>
                <td>6.04</td>
                <td>13.0%</td>
              </tr>
              <tr>
                <td>Operating System</td>
                <td>Self-Instruct</td>
                <td>647</td>
                <td>195</td>
                <td>3.85</td>
                <td>30.1%</td>
              </tr>
              <tr>
                <td rowspan="2"><br />Database</td>
                <td>Self-Instruct</td>
                <td>1,074</td>
                <td>178</td>
                <td>2.13</td>
                <td>16.6%</td>
              </tr>
              <tr>
                <td>Task Deri.</td>
                <td>5,302</td>
                <td>360</td>
                <td>2.03</td>
                <td>6.79%</td>
              </tr>
              <tr>
                <td>AgentInstruct</td>
                <td>-</td>
                <td>35,341</td>
                <td>1,866</td>
                <td>5.24</td>
                <td>5.28%</td>
              </tr>
            </tbody>
          </table>
        </div>
      </div>
    </section>

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            <h2 class="title is-3">Detailed Results</h2>
          </div>
          <br />
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          <table>
            <caption style="text-align: left">
              Main results of
              <b>AgentTuning</b
              >. Model significantly outperforms Llama 2 across different
              scales, excelling in both held-in and held-out tasks, without
              compromising its performance on general tasks. Overall stands for
              score calculated from a weighted average of all tasks within the
              same category. (API-based models and open-source models are
              compared separately.
              <b>bold</b
              >: the best in API-based models and open-source models;
              <u>underline</u
              >: the second best in open-source models)
            </caption>
            <thead>
              <tr>
                <th rowspan="2"><br />Type</th>
                <th rowspan="2"><br />Task</th>
                <th colspan="2">API-based</th>
                <th colspan="3">Llama 2 (chat)</th>
                <th colspan="3">AgentLM</th>
              </tr>
              <tr>
                <th>GPT-3.5</th>
                <th>GPT-4</th>
                <th>7B</th>
                <th>13B</th>
                <th>70B</th>
                <th>7B</th>
                <th>13B</th>
                <th>70B</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td rowspan="7"><br /><br /><br /><br /><br />Held-in Tasks</td>
                <td>ALFWorld</td>
                <td>14.0</td>
                <td><b>78.0</b></td>
                <td>2.0</td>
                <td>2.0</td>
                <td>6.0</td>
                <td><u>84.0</u></td>
                <td>76.0</td>
                <td><b>86.0</b></td>
              </tr>
              <tr>
                <td>WebShop</td>
                <td><b>67.2</b></td>
                <td>58.6</td>
                <td>4.4</td>
                <td>7.2</td>
                <td>1.5</td>
                <td>63.6</td>
                <td><b>70.8</b></td>
                <td><u>64.9</u></td>
              </tr>
              <tr>
                <td>Mind2Web</td>
                <td>15.7</td>
                <td><b>22.6</b></td>
                <td>3.7</td>
                <td>2.3</td>
                <td>0.2</td>
                <td>6.4</td>
                <td><u>8.4</u></td>
                <td><b>13.5</b></td>
              </tr>
              <tr>
                <td>KG</td>
                <td>27.2</td>
                <td><b>52.1</b></td>
                <td>0.0</td>
                <td>0.0</td>
                <td>0.0</td>
                <td>18.1</td>
                <td><u>26.8</u></td>
                <td><b>47.0</b></td>
              </tr>
              <tr>
                <td>OS</td>
                <td>32.6</td>
                <td><b>36.8</b></td>
                <td>8.3</td>
                <td>9.0</td>
                <td>9.0</td>
                <td>17.4</td>
                <td><u>18.1</u></td>
                <td><b>21.5</b></td>
              </tr>
              <tr>
                <td>Database</td>
                <td>15.0</td>
                <td><b>33.7</b></td>
                <td>0.3</td>
                <td>1.3</td>
                <td>9.3</td>
                <td>30.6</td>
                <td><u>33.7</u></td>
                <td><b>37.7</b></td>
              </tr>
              <tr>
                <td><b>Overall</b></td>
                <td>1.59</td>
                <td><b>2.75</b></td>
                <td>0.19</td>
                <td>0.20</td>
                <td>0.27</td>
                <td>1.96</td>
                <td><u>2.11</u></td>
                <td><b>2.55</b></td>
              </tr>
              <tr>
                <td rowspan="7">
                  <br /><br /><br /><br /><br /><br />Held-out Tasks
                </td>
                <td>SciWorld</td>
                <td>21.2</td>
                <td><b>36.4</b></td>
                <td>5.9</td>
                <td>6.4</td>
                <td>7.9</td>
                <td>13.7</td>
                <td><u>18.0</u></td>
                <td><b>20.8</b></td>
              </tr>
              <tr>
                <td>MiniWoB++</td>
                <td>66.7</td>
                <td><b>69.4</b></td>
                <td>0.0</td>
                <td>19.6</td>
                <td>0.7</td>
                <td>28.9</td>
                <td><u>31.1</u></td>
                <td><b>60.7</b></td>
              </tr>
              <tr>
                <td>WebArena</td>
                <td>4.56</td>
                <td><b>6.28</b></td>
                <td>1.23</td>
                <td>1.11</td>
                <td>0.62</td>
                <td>0.74</td>
                <td><u>1.60</u></td>
                <td><b>3.81</b></td>
              </tr>
              <tr>
                <td>HotpotQA</td>
                <td>37.4</td>
                <td><b>52.1</b></td>
                <td>22.6</td>
                <td>25.2</td>
                <td><u>37.5</u></td>
                <td>22.3</td>
                <td>29.6</td>
                <td><b>41.6</b></td>
              </tr>
              <tr>
                <td>ReWOO</td>
                <td>71.0</td>
                <td><b>79.7</b></td>
                <td>48.3</td>
                <td>48.7</td>
                <td>55.1</td>
                <td>50.9</td>
                <td><u>55.7</u></td>
                <td><b>66.0</b></td>
              </tr>
              <tr>
                <td>DCG</td>
                <td>24.5</td>
                <td><b>50.0</b></td>
                <td>0.0</td>
                <td>0.0</td>
                <td>5.0</td>
                <td><u>7.0</u></td>
                <td>2.5</td>
                <td><b>23.5</b></td>
              </tr>
              <tr>
                <td><b>Overall</b></td>
                <td>1.49</td>
                <td><b>2.13</b></td>
                <td>0.38</td>
                <td>0.49</td>
                <td>0.51</td>
                <td>0.67<br /><span style="color: #8f0040">(+76%)</span></td>
                <td>
                  <u>0.78</u><br /><span style="color: #8f0040">(+57%)</span>
                </td>
                <td>
                  <b>1.40</b><br /><span style="color: #8f0040">(+176%)</span>
                </td>
              </tr>
              <tr>
                <td rowspan="5" , style="border-bottom: 1px solid black">
                  <br /><br /><br /><br />General Tasks
                </td>
                <td>MMLU</td>
                <td>70.0</td>
                <td><b>86.4</b></td>
                <td>48.0</td>
                <td>54.3</td>
                <td><b>62.1</b></td>
                <td>48.7</td>
                <td>53.6</td>
                <td><u>59.5</u></td>
              </tr>
              <tr>
                <td>HumanEval</td>
                <td>48.1</td>
                <td><b>67.0</b></td>
                <td>13.9</td>
                <td>18.4</td>
                <td><b>30.8</b></td>
                <td>15.4</td>
                <td>14.8</td>
                <td><u>28.7</u></td>
              </tr>
              <tr>
                <td>GSM8K</td>
                <td>57.1</td>
                <td><b>87.1</b></td>
                <td>27.7</td>
                <td>37.5</td>
                <td><u>54.7</u></td>
                <td>24.6</td>
                <td>32.4</td>
                <td><b>59.7</b></td>
              </tr>
              <tr>
                <td>MT-Bench</td>
                <td>7.94</td>
                <td><b>8.99</b></td>
                <td>6.26</td>
                <td>6.65</td>
                <td><u>6.85</u></td>
                <td>6.11</td>
                <td>6.57</td>
                <td><b>7.26</b></td>
              </tr>
              <tr>
                <td><b>Overall</b></td>
                <td>1.15</td>
                <td><b>1.53</b></td>
                <td>0.63</td>
                <td>0.74</td>
                <td><u>0.95</u></td>
                <td>0.62<br />(-1%)</td>
                <td>0.69<br />(-7%)</td>
                <td><b>0.96</b><br />(+1%)</td>
              </tr>
            </tbody>
          </table>
        </div>
      </div>
    </section>
    <section class="hero is-small">
      <div class="hero-body">
        <div class="container is-max-desktop content">
          <div class="column has-text-centered is-fifths-fifths">
            <h2 class="title is-3">Error Analysis</h2>
          </div>
          <br />
          <center>
            <div class="item">
              <img
                src="static/images/error-analysis.svg"
                alt="error-analysis"
                width="600"
              />
            </div>
          </center>
          <br />
          To delve into error analysis, we selected three tasks from the held-in
          set (ALFWorld, WebShop, Knowledge Graph) and identified common error
          types using a rule-based approach, such as invalid actions and
          repeated generations. The results can be seen above.
          <br /><br />
          Overall, the original Llama2 exhibited more elementary mistakes like
          repetition or taking invalid actions. In contrast, GPT-3.5 and
          especially GPT-4 made fewer of such errors. However, the
          <b>AgentLM</b>
          noticeably reduced these basic errors. We speculate that while Llama 2
          chat inherently possesses agent capabilities, its poor performance
          might be due to a lack of aligned training on agent data; the
          <b>AgentTuning</b> effectively activated its agent potential.
        </div>
      </div>
    </section>
    <section class="hero">
      <div class="hero is-small">
        <div class="container is-max-desktop content">
          <div class="column has-text-centered is-fifths-fifths"></div>
          <center>
            <h2 class="title is-3">Case Study</h2>
            <div class="item">
              <img src="static/images/case-study.svg" width="800" />
            </div>
          </center>
          <br />
          <p>
            Comparison case study on ALFWorld and Knowledge Graph between
            Llama-2-70b-chat and AgentLM-70B. (a) For the ALFWorld task,
            Llama-2-70b-chat repeated the same action ultimately failing to
            complete the task, while Agent-70B adjusted its actions after a
            failure. (b) For the Knowledge Graph task, Llama-2-70b-chat refused
            to fix the function call and instead demanded the user to implement
            the function upon encountering a error. In contrast, AgentLM-70B
            provided the correct function call.
          </p>
        </div>
      </div>
    </section>

    <!--BibTex citation -->
    <section class="section" id="BibTeX">
      <div class="container is-max-desktop content">
        <h2 class="title">Reference</h2>
        Please kindly cite our paper if you use our model, data, code or
        results:
        <br />
        <br />
        <pre><code>@misc{zeng2023agenttuning,
      title={AgentTuning: Enabling Generalized Agent Abilities for LLMs}, 
      author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang},
      year={2023},
      eprint={2310.12823},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}</code></pre>
      </div>
    </section>
    <!--End BibTex citation -->

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